Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport

Mingyuan Gao, Jianli Cong, Jieling Xiao, Qing He, Shoutai Li, Yuan Wang, Ye Yao, Rong Chen and Ping Wang

Applied Energy, 2020, vol. 257, issue C

Abstract: Railway freight wagons are unpowered railway vehicles that are used for the transportation of cargoes. However, standard freight wagons are not equipped with electrical conduits (i.e. no external power source) and therefore cannot power monitoring sensors, and those monitoring sensors (pressure sensors and accelerometers) are essential for ensuring driving safety (e.g. derailment monitoring, insufficient brake pressure, and hunting movements). This paper reviews the self-powered sensor nodes for use in rail transport and proposes a technical approach for harnessing vibration energy of freight wagons to power the monitoring sensors. A multi-body rigid-flexible coupled dynamic model of freight rail transport is established to simulate the vibration response (the vibration acceleration and displacement) of freight wagons (car bodies and bogies) and railway tracks (rails and sleepers). The validity of the calculation model is verified by field tests. A new compact electromagnetic vibration energy harvester with an inertial pendulum is developed. Under the inertial oscillations caused by the wheel-rail interaction, the output power is 263 mW. A DC-DC booster circuit with supercapacitors is developed to power the sensor nodes through the harvested vibration energy. The system operates at a startup voltage of 1 V with a conversion efficiency of 40–65%. The energy harvesting system can drive an integral commercial sensor unit (with a pressure sensor and a triaxial MEMS accelerometer) and act as a self-powered sensor node for use in the condition monitoring system of freight rail transport.

Keywords: Energy harvesting; Freight rail transport; Vehicle dynamics; Self-powered sensor nodes; Renewable Energy (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.apenergy.2019.113969

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